Abstract:

We address the super-resolution problem: how to estimate missing high spatial frequency components of a static image. From a training set of full- and low- resolution images, we build a database of patches of corrsponding high- and low-frequency image information. Given a new low-resolution image to enhance, we select from the training data a set of 10 candidate high-frequency patches for each patch of the low-resolution image. We use compatibility relationships between neighboring candidates in Bayesian belief propagation to select the most probable candidate high-frequency interpretation at each image patch. The resulting estimates of the high-frequency image are good. The algorithm maintains sharp edges, and makes visually plausible guesses in regions of texture.